{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T09:13:04Z","timestamp":1772356384124,"version":"3.50.1"},"reference-count":35,"publisher":"Emerald","issue":"10","license":[{"start":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T00:00:00Z","timestamp":1599523200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IMDS"],"published-print":{"date-parts":[[2020,9,8]]},"abstract":"Purpose<\/jats:title>Tourism spot or point-of-interest (POI) recommendation has become a common service in people's daily life. The purpose of this paper is to model users' check-in history in order to predict a set of locations that a user may soon visit.<\/jats:p><\/jats:sec>Design\/methodology\/approach<\/jats:title>The authors proposed a novel learning-based method, the pattern-based dual learning POI recommendation system as a solution to consider users' interests and the uniformity of popular POI patterns when making recommendations. Differing from traditional long short-term memory (LSTM), a new users\u2019 regularity\u2013POIs\u2019 popularity patterns long short-term memory (UP-LSTM) model was developed to concurrently combine the behaviors of a specific user and common users.<\/jats:p><\/jats:sec>Findings<\/jats:title>The authors introduced the concept of dual learning for POI recommendation. Several performance evaluations were conducted on real-life mobility data sets to demonstrate the effectiveness and practicability of POI recommendations. The metrics such as hit rate, precision, recall and F<\/jats:italic>-measure were used to measure the capability of ranking and precise prediction of the proposed model over all baselines. The experimental results indicated that the proposed UP-LSTM model consistently outperformed the state-of-the-art models in all metrics by a large margin.<\/jats:p><\/jats:sec>Originality\/value<\/jats:title>This study contributes to the existing literature by incorporating a novel pattern\u2013based technique to analyze how the popularity of POIs affects the next move of a particular user. Also, the authors have proposed an effective fusing scheme to boost the prediction performance in the proposed UP-LSTM model. The experimental results and discussions indicate that the combination of the user's regularity and the POIs\u2019 popularity patterns in PDLRec could significantly enhance the performance of POI recommendation.<\/jats:p><\/jats:sec>","DOI":"10.1108\/imds-04-2020-0207","type":"journal-article","created":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T12:13:49Z","timestamp":1599567229000},"page":"1901-1921","source":"Crossref","is-referenced-by-count":8,"title":["Pattern-based dual learning for point-of-interest (POI) recommendation"],"prefix":"10.1108","volume":"120","author":[{"given":"Tipajin","family":"Thaipisutikul","sequence":"first","affiliation":[]},{"given":"Yi-Cheng","family":"Chen","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2020101902274061000_ref001","article-title":"Regression-based latent factor models","year":"2009"},{"key":"key2020101902274061000_ref002","article-title":"CLoSe: contextualized location sequence 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